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Document-Level Relation Extraction With Context Guided Mention Integration and Inter-Pair Reasoning
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DOI：10.1109/TASLP.2023.3316454.png)
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摘要
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Document-level Relation Extraction (DRE) aims to recognize the relations between two entities. The entity may correspond to multiple mentions that span beyond the sentence boundary. Few previous studies have investigated the mention integration, which may be problematic because coreferential mentions do not equally contribute to a specific relation. Moreover, prior efforts mainly focus on reasoning at entity-level rather than capturing the global interactions between the entity pairs. In this article, we propose two novel techniques, Context Guided Mention Integration and Inter-pair Reasoning (CGM2IR), to improve DRE. Instead of simply applying average pooling, the contexts are utilized to guide the integration of coreferential mentions in a weighted sum manner. Additionally, inter-pair reasoning executes an iterative algorithm on the entity pair graph, so as to model the interdependency of relations. We evaluate our CGM2IR model on three widely used benchmark datasets, namely DocRED, CDR, and GDA. Experimental results show that our model outperforms previous strong baselines and achieves remarkable effectiveness.
Keyword:
Cognition
Task analysis
Encoding
Context modeling
Transformers
Information processing
Data mining
Document-level relation extraction
inter-pair reasoning
context guided cross-attention
entity pair graph
graph nerual network
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5.1
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